Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
About the role
- Staff ML Engineer at Samsara designing and evolving end-to-end ML platform. Leading initiatives for Safety AI and driving impactful ML solutions.
Responsibilities
- Design, build, and operate Samsara’s end-to-end ML platform spanning training, experimentation, batch and online inference, and edge deployment
- Partner with product and applied ML teams to design, launch, and iterate ML-powered features (e.g., backend CV models, EcoDriving insights, LLM-based reporting)
- Lead throughput and cost estimation for new ML features—from early-stage exploration to production-scale capacity planning
- Collaborate on experiment design and evaluation, including defining success metrics, structuring A/B tests or offline evaluations
- Evolve shared training and experimentation infrastructure (e.g., job orchestration, cluster configuration, environment management)
- Design and operate scalable online and batch inference systems (Ray- and Spark-based)
- Partner with firmware and edge teams to define workflows for packaging, validating, and deploying models to Samsara devices
- Own the reliability, observability, and security posture of ML systems across cloud and edge environments
- Provide Staff+/Senior-Staff-level technical leadership by setting architecture and strategy for ML infrastructure
- Drive strong developer experience through documentation, office hours, and best practices
- Own or co-own end-to-end technical delivery for high-priority or high-risk initiatives.
Requirements
- 10+ years of overall experience in machine learning engineering or related fields
- Strong experience with distributed computing frameworks such as Ray and/or Spark
- Hands-on experience with cloud infrastructure (AWS), containers/Kubernetes, and production observability tooling
- Proven experience building or supporting ML platforms (training, experimentation, or inference) used by multiple teams
- Solid understanding of ML fundamentals including evaluation, experiment design, and model iteration in production environments.
Benefits
- Flexible working model
- Professional development stipend
- Comprehensive health and parental leave plans
- Initial RSU grant with no vesting cliff
- Ongoing refresh opportunities tied to performance
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